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Background <p>Oral squamous cell carcinoma (OSCC) is the predominant type of oral cancer, with over 370,000 new cases and approximately 170,000 deaths annually worldwide. Despite therapeutic advancements, OSCC mortality rates have been increasing, underscoring the need for improved prognostic models and therapeutic targets.</p> Methods <p>We integrated transcriptomic and clinical survival data from the TCGA-OSCC dataset to identify ferroptosis-related prognostic genes. Using weighted gene co-expression network analysis (WGCNA), we selected genes associated with OSCC prognosis and applied Lasso regression analysis to pinpoint key genes. A prognostic model was constructed and validated through survival analysis and receiver operating characteristic (ROC) curve analysis.</p> Results <p>WGCNA identified modules significantly correlated with ferroptosis, yielding 321 genes associated with OSCC prognosis. Univariate Cox analysis identified 13 genes affecting OSCC prognosis. Lasso regression and multivariate Cox regression narrowed down the gene set to a final set of 7 genes, which were used to construct the risk model. The model stratified patients into high- and low-risk groups with significant survival differences (P &lt; 0.001). The model's predictive accuracy was validated, with AUC values ranging from 0.565 to 0.733 for 1-, 3-, and 5-year survival predictions. Immune-related analysis revealed that low-risk patients exhibited higher immune cell infiltration and were more likely to benefit from immunotherapy.</p> Conclusion <p>Our study presents a novel prognostic model for OSCC patients based on ferroptosis-related genes, which not only predicts survival but also identifies potential therapeutic targets. The model's predictive accuracy and clinical relevance were validated, offering a new strategy for OSCC treatment.</p>

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WGCNA and ferroptosis genes in OSCC: unraveling prognostic biomarkers and therapeutic targets

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月24日

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  • Ke Liu,
  • Jiannan Yu,
  • Xuanxi Huang,
  • Hongyan Gao,
  • Jing Wang

摘要

Background

Oral squamous cell carcinoma (OSCC) is the predominant type of oral cancer, with over 370,000 new cases and approximately 170,000 deaths annually worldwide. Despite therapeutic advancements, OSCC mortality rates have been increasing, underscoring the need for improved prognostic models and therapeutic targets.

Methods

We integrated transcriptomic and clinical survival data from the TCGA-OSCC dataset to identify ferroptosis-related prognostic genes. Using weighted gene co-expression network analysis (WGCNA), we selected genes associated with OSCC prognosis and applied Lasso regression analysis to pinpoint key genes. A prognostic model was constructed and validated through survival analysis and receiver operating characteristic (ROC) curve analysis.

Results

WGCNA identified modules significantly correlated with ferroptosis, yielding 321 genes associated with OSCC prognosis. Univariate Cox analysis identified 13 genes affecting OSCC prognosis. Lasso regression and multivariate Cox regression narrowed down the gene set to a final set of 7 genes, which were used to construct the risk model. The model stratified patients into high- and low-risk groups with significant survival differences (P < 0.001). The model's predictive accuracy was validated, with AUC values ranging from 0.565 to 0.733 for 1-, 3-, and 5-year survival predictions. Immune-related analysis revealed that low-risk patients exhibited higher immune cell infiltration and were more likely to benefit from immunotherapy.

Conclusion

Our study presents a novel prognostic model for OSCC patients based on ferroptosis-related genes, which not only predicts survival but also identifies potential therapeutic targets. The model's predictive accuracy and clinical relevance were validated, offering a new strategy for OSCC treatment.